{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d0c63c2",
   "metadata": {},
   "source": [
    "# **<font size=4 color=#BB3D00 face=微软雅黑>信号平滑处理</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c730fa45",
   "metadata": {},
   "source": [
    "## <font size=3  face=微软雅黑>**※Matlab案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65effe8c",
   "metadata": {},
   "source": [
    "网址：https://ww2.mathworks.cn/help/signal/gs/the-sinc-function.html     \n",
    "描述：本案例由1个示例构成。\n",
    "### - <font color=DarkOrChid size=3>示例1：绘制值范围从 −5 到 5 的线性间距向量的 sinc 函数</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f17d47",
   "metadata": {},
   "source": [
    "## <font size=3 face=微软雅黑>**※Python案例**</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97a50ca",
   "metadata": {},
   "source": [
    "针对以上案例，采用Python语言实现。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5a2fff",
   "metadata": {},
   "source": [
    "### - <font color=DarkOrChid size=3>示例1：绘制值范围从 −5 到 5 的线性间距向量的 sinc 函数</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e99473",
   "metadata": {},
   "source": [
    "sinc 函数计算输入向量或矩阵 x 的数学正弦函数。作为时间或空间的函数，sinc 函数是以零为中心、宽度为 2π 并具有单位高度的频率的矩形脉冲的傅里叶逆变换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3049b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "def sinc(x):\n",
    "    def function(x): \n",
    "        if x==0:\n",
    "            return 1\n",
    "        else:\n",
    "            return np.sin(np.pi*x)/(np.pi*x)\n",
    "    return np.array([function(t) for t in x])\n",
    "x = np.linspace(-5,5)\n",
    "y=sinc(x)\n",
    "plt.plot(x,y)\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
